论文标题
图形变压器网络具有事件参数提取的句法和语义结构
Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction
论文作者
论文摘要
事件参数提取（EAE）的目标是找到给定事件触发词的每个实体提及的角色。在先前的作品中已经显示，句子的句法结构对EAE的深度学习模型有帮助。但是，这种先前工作的主要问题是，它们无法利用句子的语义结构来诱导EAE的有效表示。因此，在这项工作中，我们为EAE提出了一种新型模型，该模型通过图形变压器网络（GTN）来利用句子的句法和语义结构，以学习EAE的更有效的句子结构。此外，我们引入了一种基于信息瓶颈的新型感应偏置，以改善EAE模型的概括。进行了广泛的实验以证明所提出的模型的好处，从而导致EAE在标准数据集上的最新性能。
The goal of Event Argument Extraction (EAE) is to find the role of each entity mention for a given event trigger word. It has been shown in the previous works that the syntactic structures of the sentences are helpful for the deep learning models for EAE. However, a major problem in such prior works is that they fail to exploit the semantic structures of the sentences to induce effective representations for EAE. Consequently, in this work, we propose a novel model for EAE that exploits both syntactic and semantic structures of the sentences with the Graph Transformer Networks (GTNs) to learn more effective sentence structures for EAE. In addition, we introduce a novel inductive bias based on information bottleneck to improve generalization of the EAE models. Extensive experiments are performed to demonstrate the benefits of the proposed model, leading to state-of-the-art performance for EAE on standard datasets.
